An Exploratory Data Analysis Approach to Understand the Early Outbreak of COVID-19: China and Global Health Perspectives

This Exploratory Data Analysis and Visualization repository is solely developed for the research article submitted for evaluation in the CSI Transactions on ICT. This is only for visualization purposes and all the visualization models made from the data sources provided by different Organizations. Here we present an approach to visualize and analyze the data between 22 January 2020 and 4 March 2020.

Visual Exploratory Data Analysis (VEDA) of COVID-19 outbreak, caused by SARS-COV-2

Samrat Kumar Dey, Md. Mahbubur Rahaman, Umme Raihan Siddiqi, and Arpita Howlader

Exploratory Data Analysis (EDA)

Country wise updated data till 4 March 2020

In [8]:
 
Out[8]:
Confirmed Deaths Recovered
Country/Region Province/State
Afghanistan NA 1 0 0
Algeria NA 12 0 0
Andorra NA 1 0 0
Argentina NA 1 0 0
Armenia NA 1 0 0
Australia From Diamond Princess 0 0 0
New South Wales 22 1 4
Northern Territory 1 0 0
Queensland 11 0 1
South Australia 5 0 2
Tasmania 1 0 0
Victoria 10 0 4
Western Australia 2 1 0
Austria NA 29 0 0
Azerbaijan NA 3 0 0
Bahrain NA 52 0 0
Belarus NA 6 0 0
Belgium NA 23 0 1
Brazil NA 4 0 0
Cambodia NA 1 0 1
Canada Montreal, QC 1 0 0
British Columbia 12 0 3
London, ON 1 0 1
Toronto, ON 19 0 2
Chile NA 1 0 0
China Anhui 990 6 956
Beijing 418 8 297
Chongqing 576 6 502
Fujian 296 1 270
Gansu 91 2 87
Guangdong 1350 7 1133
Guangxi 252 2 210
Guizhou 146 2 114
Hainan 168 5 158
Hebei 318 6 301
Heilongjiang 480 13 373
Henan 1272 22 1234
Hubei 67332 2871 38557
Hunan 1018 4 916
Inner Mongolia 75 1 63
Jiangsu 631 0 577
Jiangxi 935 1 884
Jilin 93 1 86
Liaoning 125 1 106
Ningxia 75 0 69
Qinghai 18 0 18
Shaanxi 245 1 223
Shandong 758 6 516
Shanghai 338 3 298
Shanxi 133 0 124
Sichuan 538 3 406
Tianjin 136 3 124
Tibet 1 0 1
Xinjiang 76 3 69
Yunnan 174 2 169
Zhejiang 1213 1 1114
Croatia NA 10 0 0
Czech Republic NA 8 0 0
Denmark NA 10 0 0
Dominican Republic NA 1 0 0
Ecuador NA 10 0 0
Egypt NA 2 0 1
Estonia NA 2 0 0
Faroe Islands NA 1 0 0
Finland NA 6 0 1
France NA 285 4 12
Georgia NA 3 0 0
Germany NA 262 0 16
Gibraltar NA 1 0 0
Greece NA 9 0 0
Hong Kong Hong Kong 105 2 37
Hungary NA 2 0 0
Iceland NA 26 0 0
India NA 28 0 3
Indonesia NA 2 0 0
Iran NA 2922 92 552
Iraq NA 35 2 0
Ireland NA 6 0 0
Israel NA 15 0 1
Italy NA 3089 107 276
Japan NA 331 6 43
Jordan NA 1 0 0
Kuwait NA 56 0 0
Latvia NA 1 0 0
Lebanon NA 13 0 1
Liechtenstein NA 1 0 0
Lithuania NA 1 0 0
Luxembourg NA 1 0 0
Macau Macau 10 0 9
Malaysia NA 50 0 22
Mexico NA 5 0 1
Monaco NA 1 0 0
Morocco NA 1 0 0
Nepal NA 1 0 1
Netherlands NA 38 0 0
New Zealand NA 3 0 0
Nigeria NA 1 0 0
North Macedonia NA 1 0 0
Norway NA 56 0 0
Oman NA 15 0 2
Pakistan NA 5 0 0
Philippines NA 3 1 1
Poland NA 1 0 0
Portugal NA 5 0 0
Qatar NA 8 0 0
Romania NA 4 0 1
Russia NA 3 0 2
Saint Barthelemy NA 3 0 0
San Marino NA 16 1 0
Saudi Arabia NA 1 0 0
Senegal NA 4 0 0
Singapore NA 110 0 78
South Korea NA 5621 35 41
Spain NA 222 2 2
Sri Lanka NA 1 0 1
Sweden NA 35 0 0
Switzerland NA 90 0 3
Taiwan Taiwan 42 1 12
Thailand NA 43 1 31
Tunisia NA 1 0 0
UK NA 85 0 8
US Norfolk County, MA 1 0 0
Berkeley, CA 1 0 0
Boston, MA 1 0 1
Contra Costa County, CA 1 0 0
Cook County, IL 4 0 2
Fulton County, GA 2 0 0
Grafton County, NH 2 0 0
Hillsborough, FL 2 0 0
Humboldt County, CA 1 0 0
King County, WA 31 9 1
Lackland, TX (From Diamond Princess) 0 0 0
Los Angeles, CA 7 0 0
Madison, WI 1 0 1
Maricopa County, AZ 1 0 0
New York City, NY 1 0 0
Omaha, NE (From Diamond Princess) 0 0 0
Orange County, CA 3 0 0
Placer County, CA 2 1 0
Providence, RI 2 0 0
Sacramento County, CA 2 0 0
San Antonio, TX 1 0 0
San Benito, CA 2 0 0
San Diego County, CA 2 0 1
San Mateo, CA 2 0 0
Santa Clara, CA 11 0 1
Sarasota, FL 1 0 0
Snohomish County, WA 8 1 0
Sonoma County, CA 1 0 0
Tempe, AZ 1 0 1
Travis, CA (From Diamond Princess) 0 0 0
Umatilla, OR 1 0 0
Unassigned Location (From Diamond Princess) 45 0 0
Wake County, NC 1 0 0
Washington County, OR 2 0 0
Westchester County, NY 10 0 0
Ukraine NA 1 0 0
United Arab Emirates NA 27 0 5
Vietnam NA 16 0 16

World Wide updated data till 4 March 2020

In[10]:
 
Out[10]:
Country/Region Confirmed Deaths Recovered
0 China 80271 2981 49955
1 South Korea 5621 35 41
2 Italy 3089 107 276
3 Iran 2922 92 552
4 Japan 331 6 43
5 France 285 4 12
6 Germany 262 0 16
7 Spain 222 2 2
8 US 153 11 8
9 Singapore 110 0 78
10 Hong Kong 105 2 37
11 Switzerland 90 0 3
12 UK 85 0 8
13 Norway 56 0 0
14 Kuwait 56 0 0
15 Bahrain 52 0 0
16 Australia 52 2 11
17 Malaysia 50 0 22
18 Thailand 43 1 31
19 Taiwan 42 1 12
20 Netherlands 38 0 0
21 Sweden 35 0 0
22 Iraq 35 2 0
23 Canada 33 0 6
24 Austria 29 0 0
25 India 28 0 3
26 United Arab Emirates 27 0 5
27 Iceland 26 0 0
28 Belgium 23 0 1
29 San Marino 16 1 0
30 Vietnam 16 0 16
31 Israel 15 0 1
32 Oman 15 0 2
33 Lebanon 13 0 1
34 Algeria 12 0 0
35 Denmark 10 0 0
36 Croatia 10 0 0
37 Ecuador 10 0 0
38 Macau 10 0 9
39 Greece 9 0 0
40 Qatar 8 0 0
41 Czech Republic 8 0 0
42 Belarus 6 0 0
43 Finland 6 0 1
44 Ireland 6 0 0
45 Portugal 5 0 0
46 Pakistan 5 0 0
47 Mexico 5 0 1
48 Senegal 4 0 0
49 Brazil 4 0 0
50 Romania 4 0 1
51 Azerbaijan 3 0 0
52 Saint Barthelemy 3 0 0
53 Russia 3 0 2
54 New Zealand 3 0 0
55 Georgia 3 0 0
56 Philippines 3 1 1
57 Estonia 2 0 0
58 Indonesia 2 0 0
59 Hungary 2 0 0
60 Egypt 2 0 1
61 Ukraine 1 0 0
62 Armenia 1 0 0
63 Chile 1 0 0
64 Andorra 1 0 0
65 Tunisia 1 0 0
66 Sri Lanka 1 0 1
67 Cambodia 1 0 1
68 Argentina 1 0 0
69 Dominican Republic 1 0 0
70 Saudi Arabia 1 0 0
71 Poland 1 0 0
72 North Macedonia 1 0 0
73 Nigeria 1 0 0
74 Nepal 1 0 1
75 Morocco 1 0 0
76 Monaco 1 0 0
77 Luxembourg 1 0 0
78 Lithuania 1 0 0
79 Liechtenstein 1 0 0
80 Latvia 1 0 0
81 Jordan 1 0 0
82 Faroe Islands 1 0 0
83 Gibraltar 1 0 0
84 Afghanistan 1 0 0

Countries with deaths reported

In [12]:
 
Out[12]:
Country/Region Confirmed Deaths Recovered
0 Norway 56 0 0
1 Kuwait 56 0 0
2 Bahrain 52 0 0
3 Netherlands 38 0 0
4 Iraq 35 2 0
5 Sweden 35 0 0
6 Austria 29 0 0
7 Iceland 26 0 0
8 San Marino 16 1 0
9 Algeria 12 0 0
10 Denmark 10 0 0
11 Ecuador 10 0 0
12 Croatia 10 0 0
13 Greece 9 0 0
14 Czech Republic 8 0 0
15 Qatar 8 0 0
16 Belarus 6 0 0
17 Ireland 6 0 0
18 Portugal 5 0 0
19 Pakistan 5 0 0
20 Brazil 4 0 0
21 Senegal 4 0 0
22 Saint Barthelemy 3 0 0
23 Georgia 3 0 0
24 Azerbaijan 3 0 0
25 New Zealand 3 0 0
26 Indonesia 2 0 0
27 Hungary 2 0 0
28 Estonia 2 0 0
29 Saudi Arabia 1 0 0
30 Poland 1 0 0
31 Tunisia 1 0 0
32 North Macedonia 1 0 0
33 Nigeria 1 0 0
34 Afghanistan 1 0 0
35 Morocco 1 0 0
36 Monaco 1 0 0
37 Luxembourg 1 0 0
38 Lithuania 1 0 0
39 Liechtenstein 1 0 0
40 Latvia 1 0 0
41 Jordan 1 0 0
42 Gibraltar 1 0 0
43 Faroe Islands 1 0 0
44 Dominican Republic 1 0 0
45 Chile 1 0 0
46 Armenia 1 0 0
47 Argentina 1 0 0
48 Andorra 1 0 0
49 Ukraine 1 0 0

Data from different China provinces

In [16]:
 
Out[16]:
Province/State Confirmed Deaths Recovered
0 Hubei 67332 2871 38557
1 Guangdong 1350 7 1133
2 Henan 1272 22 1234
3 Zhejiang 1213 1 1114
4 Hunan 1018 4 916
5 Anhui 990 6 956
6 Jiangxi 935 1 884
7 Shandong 758 6 516
8 Jiangsu 631 0 577
9 Chongqing 576 6 502
10 Sichuan 538 3 406
11 Heilongjiang 480 13 373
12 Beijing 418 8 297
13 Shanghai 338 3 298
14 Hebei 318 6 301
15 Fujian 296 1 270
16 Guangxi 252 2 210
17 Shaanxi 245 1 223
18 Yunnan 174 2 169
19 Hainan 168 5 158
20 Guizhou 146 2 114
21 Tianjin 136 3 124
22 Shanxi 133 0 124
23 Liaoning 125 1 106
24 Jilin 93 1 86
25 Gansu 91 2 87
26 Xinjiang 76 3 69
27 Ningxia 75 0 69
28 Inner Mongolia 75 1 63
29 Qinghai 18 0 18
30 Tibet 1 0 1

Visual Exploratory Data Analysis (V-EDA)

Number of Confirmed and Deaths cases

In[21]:
 

Across the globe

In [63]:
 
Out[63]:

Reported cases in China

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Out[23]:

Affected Countries

In [24]:
 

Spread over the time

In [25]:
 

Number of Places to which COVID-19 Spread

In [26]:
 

Diamond Princess Cruise Ship

In[27]:
 
Out[27]:
Province/State Confirmed Deaths Recovered
0 Diamond Princess cruise ship 706 6 10
In [28]:
 
Out[28]:

Cases over the time

In [29]:
 

Spread over the time

In [30]:
 

Hubei - China - World

In [31]:
 
In [71]:
 

Number of new cases everyday

In [33]:
 

Recovery and Mortality Rate Over The Time

In [75]:
 

Proportion of Cases

In [57]:
 
In [37]:
 

Confirmed cases in each Countries

New cases Everyday

In [47]:
 
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  Samrat Kumar Dey, Md. Mahbubur Rahaman, Umme Raihan Siddiqi, and Arpita Howlader